What happens when a GEO experiment doubles your brand’s AI mentions and citations—but still cannot tell you whether customers can find your individual locations?

Over 90 days, we tested how structured, answer-focused content influenced visibility across ChatGPT, Gemini, and Perplexity.

The playbook worked: distributing consistent, corroborated content across multiple formats helped AI understand, trust, and recommend the brand.

But for multi-location businesses, that is only half the story.

Most GEO strategies—and most AI visibility metrics—treat a brand as one national entity. Real consumer queries such as “best hearing clinic near me” or “best tacos in San Diego” do not.

AI rebuilds the competitive landscape for every location, using different competitors, sources, reviews, and local signals to produce each answer.

This talk reveals what our experiment taught us about influencing AI visibility—and why strong aggregate share of voice can still hide hundreds of local losses.

Attendees will learn how to move from brand-level GEO measurement to location-level visibility, identify where individual locations disappear from AI answers, and apply the principles of consistency, repetition, and corroboration at local scale.

Stop optimizing the average. Start winning the map.

As AI agents start doing the searching, comparing, and buying for us, they lean on reviews to decide what to surface and recommend.

That makes review authenticity matter far more than it did when humans were the ones reading them.

In this talk, Michael explains how fake and manipulated reviews work their way into the data agents trust, what that does to the businesses and buyers on the receiving end, and why authenticity is becoming the foundation of how agentic search actually works.

He'll show what's happening in the review ecosystem right now and what marketers need to do about it before the agents make the call for them.